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A community detection algorithm based on multi-similarity method

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Abstract

Social network detection and identification constitute an important topic in the field of sociology. Previous graph similarity has focus on either the topological structure of graph or the feature value of vertex. In this work, a multi-similarity measure method for community is described. The approach devised by using multi-similarity properties based on vertex features, relationship density and topology structure, and therefore is can be formulated and extended to practical implementation. The framework of community detection combines K-means clustering, spectral clustering and modularity algorithm-making it an effective basis for the realization of a social network interpretation. With this scheme, three evaluation criteria are proposed for methodology determination. The experimental results show a better working performance of the recommended method than traditional algorithms via statistical analysis.

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Acknowledgements

The research was partially funded by the Key Program of National Natural Science Foundation of China (Grant Nos. 61133005, 61432005), the National Natural Science Foundation of China (Grant Nos. 61370095, 61472124, 61572175), International Science & Technology Cooperation Program of China (2015DFA11240). The work was supported by research Project of the Education Department of Hunan Province (Grant No. 14c0210).

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Authors and Affiliations

  1. College of Computer Science and Electric Engineering, Hunan University, Changsha, 410082, Hunan, China

    Li Ni, Pen ManMan, Jiang Wenjun & Li Kenli

  2. College of Information and Electronic Engineering, Hunan City College, Yiyang, 413000, Hunan, China

    Li Ni

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  1. Li Ni
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  2. Pen ManMan
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  3. Jiang Wenjun
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  4. Li Kenli
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Correspondence to Pen ManMan.

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Ni, L., ManMan, P., Wenjun, J. et al. A community detection algorithm based on multi-similarity method. Cluster Comput 22 (Suppl 2), 2865–2874 (2019). https://doi.org/10.1007/s10586-017-1610-0

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  • DOI: https://doi.org/10.1007/s10586-017-1610-0

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